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Vladislav Ayzenberg, Sami Yousif, Stella Lourenco; The medial axis as a robust model of object representation. Journal of Vision 2016;16(12):169. doi: 10.1167/16.12.169.
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© ARVO (1962-2015); The Authors (2016-present)
The algorithm with which humans represent objects must be durable enough to support object recognition across changes in orientation and partial occlusions. This algorithm must also be flexible enough to include both internal components of an object and its global shape. The current study examined viable models of object representation. In a first experiment, we tested the medial axis model (i.e., shape skeleton; Blum, 1973) against a principal axis model (Marr & Nishihara, 1978) with three shapes (rectangle, square, and T) using the "tap" paradigm by Firestone and Scholl (2014) where participants were instructed to tap once within a shape anywhere they choose. We collected 200 taps per shape and found that responses were significantly closer to the medial axis than either randomly-determined points (best set of 50,000 simulations; ps < .001) or points corresponding to the major principal axis (ps < .001). Having found evidence for the medial axis model, in a second experiment we tested whether an internal protrusion of varying size affected participants' the medial axis representation of a rectangle. Participants tapped within a rectangle that contained either a large or small visible obstacle within it. We found that in both cases, participants' taps conformed to the medial axis of the shape (p < .001); that is, taps accommodated to the obstacle within the rectangle. Taken together, these results provide evidence for a robust medial axis representation that is both evident for different shapes and one that flexibly accommodates to even slight protrusions within a shape.
Meeting abstract presented at VSS 2016
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